A systematic comparison of various statistical alignment models
Computational Linguistics
Evaluating translational correspondence using annotation projection
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Bootstrapping parsers via syntactic projection across parallel texts
Natural Language Engineering
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The best of two worlds: cooperation of statistical and rule-based taggers for Czech
ACL '07 Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Dependency grammar induction via bitext projection constraints
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Parser adaptation and projection with quasi-synchronous grammar features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Dependency parsing and projection based on word-pair classification
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Induction of dependency structures based on weighted projection
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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For many languages, we are not able to train any supervised parser, because there are no manually annotated data available. This problem can be solved by using a parallel corpus with English, parsing the English side, projecting the dependencies through word-alignment connections, and training a parser on the projected trees. In this paper, we introduce a simple algorithm using a combination of various wordalignment symmetrizations. We prove that our method outperforms previous work, even though it uses McDonald's maximum-spanning-tree parser as it is, without any "unsupervised" modifications.